Integrating serum pharmacochemistry, network pharmacology and untargeted metabolomics strategies to reveal the material basis and mechanism of action of Feining keli in the treatment of chronic bronchitis.

ETHNOPHARMACOLOGICAL RELEVANCE: Feining keli (FNKL) is herbal preparation mainly made from Senecio cannabifolius Less., In recent years, more and more studies have found that FNKL has excellent therapeutic effects on chronic bronchitis (CB). Nevertheless, its pharmacodynamic material basis and mechanism of action are still unknown. AIM OF THE STUDY: This study aimed to explore the pharmacodynamic material basis and mechanism of action of FNKL in treating CB. MATERIALS AND METHODS: The CB rat model was induced using nasal drops of lipopolysaccharide (LPS) in combination with smoking. Various assessments including behavioral and body mass examination, lung index measurement, enzyme linked immunosorbent assay (ELISA), as well as histological analyses using hematoxylin and eosin (H&E) and Masson staining were conducted to validate the reliability of the CB model. The serum components of FNKL in CB rats were identified using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). Network pharmacology was used to predict the network of action of the active ingredients in FNKL based on these serum components. Signaling pathways were enriched and analyzed, and molecular docking was conducted for key targets. Molecular dynamics simulations were performed using GROMACS software. The mechanism was confirmed through a series of experiments including Western blot (WB), immunofluorescence (IF), and reverse transcription (RT)-PCR. Additionally, untargeted metabolomics was employed to identify biomarkers and relevant metabolic pathways associated with the treatment of CB with FNKL. RESULTS: In CB rats, FNKL improved body mass, lung index, and pathological damage of lung tissues. It also decreased interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α), malonaldehyde (MDA) levels, and percentage of lung collagen fiber area. Furthermore, FNKL increased IL-10 and superoxide dismutase (SOD) levels, which helped alleviate bronchial inflammation in the lungs. A total of 70 FNKL chemical components were identified in CB rat serum. Through network pharmacology analysis, 5 targets, such as PI3K, AKT, NF-κB, HIF-1α, and MYD88, were identified as key targets of FNKL in the treatment of CB. Additionally, the key signaling pathways identified were PI3K/AKT pathway、NF-κB/MyD88 pathway、HIF-1α pathway. WB, IF, and RT-PCR experiments were conducted to confirm the findings. Molecular docking studies demonstrated successful docking of 16 potential active components with 5 key targets. Additionally, molecular dynamics simulations indicated the stability of quercetin-3-galactoside and HIF-1α. Metabolomics analysis revealed that FNKL primarily regulated pathways related to alpha-linolenic acid metabolism, primary bile acid biosynthesis, bile secretion, arachidonic acid metabolism, neuroactive ligand-receptor interaction, and folate biosynthesis. Furthermore, the expression levels of traumatic acid, traumatin, alpha linolenic acid, cholic acid, 2-arachidonoylglycerol, deoxycholic acid, 7,8-dihydroneopterin, and other metabolites were found to be regulated. CONCLUSION: FNKL exhibits positive therapeutic effects on CB, with quercetin-3-galactoside identified as a key active component. The mechanism of FNKL's therapeutic action on CB involves reducing inflammatory response, oxidative stress, and regulating metabolism, and its molecular mechanism was better elucidated in a holistic manner. This study serves as a reference for understanding the pharmacodynamic material basis and mechanism of action of FNKL in treating CB, and provides avenues for exploring the effects of compounded herbal medicines on CB.

Robust autofocus method based on patterned active illumination and image cross-correlation analysis.

For the effectiveness of a computer-aided diagnosis system, the quality of whole-slide image (WSI) is the foundation, and a useful autofocus method is an important part of ensuring the quality of WSI. The existing autofocus methods need to balance focusing speed and focusing accuracy, and need to be optimized separately for different samples or scenes. In this paper, a robust autofocus method based on fiber bundle illumination and image normalization analysis is proposed. For various application scenes, it meets the requirements of autofocusing through active illumination, such as bright field imaging and fluorescence imaging. For different structures on samples, it ensures the autofocusing accuracy through image analysis. The experimental results imply that the autofocusing method in this paper can effectively track the change of the distance from the sample to the focal plane and significantly improve the WSI quality.

LuckyProfiler: an ImageJ plug-in capable of quantifying FWHM resolution easily and effectively for super-resolution images.

Quantifying the resolution of a super-resolution image is vital for biologists trying to apply super-resolution microscopy in various research fields. Among the reported image resolution estimation methods, the one that calculates the full width at half maximum (FWHM) of line profile, called FWHM resolution, continues the traditional resolution criteria and has been popularly used by many researchers. However, quantifying the FWHM resolution of a super-resolution image is a time-consuming, labor-intensive, and error-prone process because this method typically involves a manual and careful selection of one or several of the smallest structures. In this paper, we investigate the influencing factors in FWHM resolution quantification systematically and present an ImageJ plug-in called LuckyProfiler for biologists so that they can have an easy and effective way of quantifying the FWHM resolution of super-resolution images.